Residential load scheduling considering maximum demand using binary particle swarm optimisation

Remani, T.; Jasmin, E A; Ahamed, T. P. Imthias

doi:10.1504/ijaip.2020.108758

Cited by 1 publication

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The traditional linear and dynamic programming algorithm of DSM [6], [7], [8] depicts significant reduction in considered parameters but unable to handle huge number of controllable devices of different types having complex constraints [21]. Some papers has given more importance to consumers energy cost reduction rather than utility in case of home energy management [12], [15], [16], [17], [18], [24], [25]. Many researchers have taken peak load [20], [21], [22], [23] as main objective because literature portrays that utility profit is more essential rather than consumer as utility is the utmost importance for consumer services.…”

Section: Related Work and Motivationmentioning

confidence: 99%

“…In all the available DSM techniques load shifting provides a promising solution that incorporates maximum benefits and flexibility to end users and generates good revenue to concern utility [11] The objective of energy cost minimization of the consumer is validated using Binary Particle Swarm Optimization (PSO) with reduced maximum demand on utility. As the alteration of consumer loads takes place to lower electricity tariff hours the maximum demand on utility decreases inherently [12]. In [13] a hybrid GA-PSO algorithm is used to reduce the cost of energy by optimal allocation of generations and loads in a day ahead market.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Grey Wolf Optimization Based Demand Side Management in Solar PV Integrated Smart Grid Environment

Yadav

Hrisheekesha²,

Bhadoria

2023

IEEE Access

View full text Add to dashboard Cite

Most of world's electrical energy demand is fulfilled by natural resources (Oil, Coal, Gas, etc.) and there is a huge gap between demand and supply. Therefore, utilities are facing the problem of peak load burden. Broadly speaking, to sustain future electricity demand renewable/alternate sources (solar, wind etc.) of energy must be integrated with smart grid (SG) to cope with energy demand. These sources are freely available, inexhaustible and can be used as an alternate source of energy. For smart utilization of electrical energy, a balance between supply and demand is required at all instants of time. In the SG environment, the most promising solution to reduce the peak load burden on utility is demand side management (DSM) which is possible because of the property of smart grid inertia. DSM permits all types of consumers to alter their energy consumption pattern to reduce the cost of energy and it helps the utility to reduce peak load burden and reshape load profile. In this study, DSM has been formulated as a single objective minimization problem to reduce peak load burden on utility. Although several optimization techniques has been listed in the literature which reduces the peak load and cost of energy, but integration of renewable energy is limited to residential consumers only. In this paper, a robust optimization algorithm inspired by the lifestyle of grey wolves, popularly known as grey wolf optimization (GWO) algorithm is utilized to solve the proposed DSM minimization problem. The DSM minimization problem optimization using GWO is demonstrated on three different cases-residential, commercial, and industrial loads in time of use (TOU) pricing scheme with and without solar PV energy (SPVE). Validation of GWO displays remarkable reductions in peak load on utility and cost of energy of consumers with and without SPVE. Also, GWO optimization results are compared with existing research papers having identical data sets.INDEX TERMS Demand side management, smart grid, appliance scheduling, grey wolf optimization, solar PV energy, time of use pricing, peak load, cost of energy.

show abstract